code: def intersection_over_union(prediction, groundtruth):
      FP, FN, TP, TN = numeric_score(prediction, groundtruth)
      if (TP + FP + FN) <= 0.0:
          return 0.0
      return TP / (TP + FP + FN) * 100.0
  
file_path: medicaltorch/medicaltorch/metrics.py
imports: from collections import defaultdict
  from scipy import spatial
  import numpy
label_desc: Use image node_id 8 for calling a global function with example usage: intersection_over_union(prediction, groundtruth) and returns: unknown, int